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Discovering patterns in categorical time series using IFS

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  • Weiß, Christian H.
  • Göb, Rainer

Abstract

The detection of patterns in categorical time series data is an important task in many fields of science. Several efficient algorithms for finding frequent sequential patterns have been proposed. An online-approach for sequential pattern analysis based on transforming the categorical alphabet to real vectors and generating fractals by an iterated function systems (IFS) is suggested. Sequential patterns can be analyzed with standard methods of cluster analysis using this approach. A version of the procedure allows detecting patterns visually.

Suggested Citation

  • Weiß, Christian H. & Göb, Rainer, 2008. "Discovering patterns in categorical time series using IFS," Computational Statistics & Data Analysis, Elsevier, vol. 52(9), pages 4369-4379, May.
    • Handle: RePEc:eee:csdana:v:52:y:2008:i:9:p:4369-4379
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    References listed on IDEAS

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    1. Liu, Lon-Mu & Bhattacharyya, Siddhartha & Sclove, Stanley L. & Chen, Rong & Lattyak, William J., 2001. "Data mining on time series: an illustration using fast-food restaurant franchise data," Computational Statistics & Data Analysis, Elsevier, vol. 37(4), pages 455-476, October.
    2. Bock, Hans H., 1996. "Probabilistic models in cluster analysis," Computational Statistics & Data Analysis, Elsevier, vol. 23(1), pages 5-28, November.
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